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A Bayesian approach to earthquake so...

Minson, Sarah.

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A Bayesian approach to earthquake source studies.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A Bayesian approach to earthquake source studies./
Author:	Minson, Sarah.
Description:	167 p.
Notes:	Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
Contained By:	Dissertation Abstracts International75-01B(E).
Subject:	Geophysics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3597166
ISBN:	9781303448775

A Bayesian approach to earthquake source studies.
Minson, Sarah.

A Bayesian approach to earthquake source studies. - 167 p.

Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.

Thesis (Ph.D.)--California Institute of Technology, 2010.

Bayesian sampling has several advantages over conventional optimization approaches to solving inverse problems. It produces the distribution of all possible models sampled proportionally to how much each model is consistent with the data and the specified prior information, and thus images the entire solution space, revealing the uncertainties and trade-offs in the model. Bayesian sampling is applicable to both linear and non-linear modeling, and the values of the model parameters being sampled can be constrained based on the physics of the process being studied and do not have to be regularized. However, these methods are computationally challenging for high-dimensional problems.

ISBN: 9781303448775Subjects--Topical Terms:

535228
Geophysics.

A Bayesian approach to earthquake source studies.
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020 $a 9781303448775
035 $a (MiAaPQ)AAI3597166
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040 $a MiAaPQ $c MiAaPQ
100 1 $a Minson, Sarah. $3 2102279
245 1 2 $a A Bayesian approach to earthquake source studies.
300 $a 167 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-01(E), Section: B.
500 $a Adviser: Mark Simons.
502 $a Thesis (Ph.D.)--California Institute of Technology, 2010.
520 $a Bayesian sampling has several advantages over conventional optimization approaches to solving inverse problems. It produces the distribution of all possible models sampled proportionally to how much each model is consistent with the data and the specified prior information, and thus images the entire solution space, revealing the uncertainties and trade-offs in the model. Bayesian sampling is applicable to both linear and non-linear modeling, and the values of the model parameters being sampled can be constrained based on the physics of the process being studied and do not have to be regularized. However, these methods are computationally challenging for high-dimensional problems.
520 $a Until now the computational expense of Bayesian sampling has been too great for it to be practicable for most geophysical problems. I present a new parallel sampling algorithm called CATMIP for Cascading Adaptive Tempered Metropolis In Parallel. This technique, based on Transitional Markov chain Monte Carlo, makes it possible to sample distributions in many hundreds of dimensions, if the forward model is fast, or to sample computationally expensive forward models in smaller numbers of dimensions. The design of the algorithm is independent of the model being sampled, so CATMIP can be applied to many areas of research.
520 $a I use CATMIP to produce a finite fault source model for the 2007 Mw 7.7 Tocopilla, Chile earthquake. Surface displacements from the earthquake were recorded by six interferograms and twelve local high-rate GPS stations. Because of the wealth of near-fault data, the source process is well-constrained. I find that the near-field high-rate GPS data have significant resolving power above and beyond the slip distribution determined from static displacements. The location and magnitude of the maximum displacement are resolved. The rupture almost certainly propagated at sub-shear velocities. The full posterior distribution can be used not only to calculate source parameters but also to determine their uncertainties. So while kinematic source modeling and the estimation of source parameters is not new, with CATMIP I am able to use Bayesian sampling to determine which parts of the source process are well-constrained and which are not.
590 $a School code: 0037.
650 4 $a Geophysics. $3 535228
650 4 $a Applied Mathematics. $3 1669109
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710 2 $a California Institute of Technology. $b Geophysics. $3 2102230
773 0 $t Dissertation Abstracts International $g 75-01B(E).
790 $a 0037
791 $a Ph.D.
792 $a 2010
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3597166